class_docstr: MultimodalNNTrainer, serves as a PyTorch Lightning trainer for multimodal models. It is designed to handle
  training, validation, and testing steps for multimodal data using specified models, optimization algorithms, and loss functions.
     For each training, validation, and test step, the trainer class computes the model's loss and accuracy and logs
     these metrics. This trainer simplifies the process of training complex multimodal models, allowing the user to
     focus on model architecture and hyperparameter tuning.
     This trainer is flexible and can be used with various models, optimizers, and loss functions, enabling its use
     across a wide range of multimodal learning tasks.
  Args:
      encoders (List[nn.Module]): A list of PyTorch `nn.Module` encoders, with one encoder per modality. Each encoder
      is responsible for transforming the raw input of a single modality into a high-level representation.
      fusion (nn.Module): A PyTorch `nn.Module` that merges the high-level representations from each modality into a
      single representation.
      head (nn.Module): A PyTorch `nn.Module` that takes the fused representation and outputs a class prediction.
      is_packed (bool, optional):  whether the input modalities are packed in one list or not (default is False, which
      means we expect input of [tensor(20xmodal1_size),(20xmodal2_size),(20xlabel_size)] for batch size 20 and 2 input
      modalities)
      optim (torch.optim, optional): The optimization algorithm to use. Defaults to torch.optim.SGD.
      lr (float, optional): Learning rate for the optimizer. Defaults to 0.001.
      weight_decay (float, optional): Weight decay for the optimizer. Defaults to 0.0.
      objective (torch.nn.Module, optional): Loss function. Defaults to torch.nn.CrossEntropyLoss.
code: def compute_loss(self, batch, split_name="valid"):
      out = self.forward(batch)
      if len(batch[-1].size()) == len(out.size()):
          truth1 = batch[-1].squeeze(len(out.size()) - 1)
      else:
          truth1 = batch[-1]
      loss = self.objective(out, truth1.long())
      pred = torch.argmax(out, dim=1)
      accuracy = accuracy_score(
          truth1.cpu().numpy(), pred.cpu().numpy()
      )
  
      return loss, {"loss": loss.item(), "accuracy": accuracy}
  
imports: import pytorch_lightning
  import torch
  import torch.nn
  from sklearn.metrics import accuracy_score
  from kale.predict import losses
label_desc: Use image node_id 3 for calling the MultimodalNNTrainer obj's underlying member method code with example usage: obj.compute_loss(batch, split_name) and returns: loss,  dict